Update app.py

app.py

@@ -1,87 +1,87 @@
-# import gradio as gr
-# from transformers import AutoModelForSequenceClassification, AutoTokenizer
-# import torch
-# from sentence_transformers import SentenceTransformer, models
-# param_max_length=256
+import gradio as gr
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import torch
+from sentence_transformers import SentenceTransformer, models
+param_max_length=256
     
-# # Define a function that takes a text input and returns the result
-# def analyze_text(input):
-#     # Your processing or model inference code here
-#     result = predict_similarity(input)
-#     return result
-
-# param_model_name="CAMeL-Lab/bert-base-arabic-camelbert-msa-sixteenth"
-
-# tokenizer = AutoTokenizer.from_pretrained(param_model_name)
-
-# class BertForSTS(torch.nn.Module):
-
-#     def __init__(self):
-#         super(BertForSTS, self).__init__()
-#         #self.bert = models.Transformer('bert-base-uncased', max_seq_length=128)
-#         #self.bert = AutoModelForSequenceClassification.from_pretrained("CAMeL-Lab/bert-base-arabic-camelbert-msa-sixteenth")
-#         self.bert = models.Transformer(param_model_name, max_seq_length=param_max_length)
-
-
-#         dimension= self.bert.get_word_embedding_dimension()
-#         #print(dimension)
-#         self.pooling_layer = models.Pooling(dimension)
-#         self.dropout = torch.nn.Dropout(0.1)
-
-#         # relu activation function
-#         self.relu =  torch.nn.ReLU()
-
-#         # dense layer 1
-#         self.fc1 = torch.nn.Linear(dimension,512)
-
-#         # dense layer 2 (Output layer)
-#         self.fc2 = torch.nn.Linear(512,512)
-#         #self.pooling_layer = models.Pooling(self.bert.config.hidden_size)
-#         self.sts_bert = SentenceTransformer(modules=[self.bert,self.pooling_layer, self.fc1])
-#         #self.sts_bert = SentenceTransformer(modules=[self.bert,self.pooling_layer, self.fc1, self.relu, self.dropout,self.fc2])
-#     def forward(self, input_data):
-#         #print(input_data)
-#         x=self.bert(input_data)
-#         x=self.pooling_layer(x)
-#         x=self.fc1(x['sentence_embedding'])
-#         x = self.relu(x)
-#         x = self.dropout(x)
-#         #x = self.fc2(x)
-
-#         return x
-
-import requests
-
-file_url = "https://drive.google.com/uc?export=download&id=1lDfR_B3fYM_rmC8H_HDtRdxJKveYWmOp"
-response = requests.get(file_url)
-
-with open("model.pt", "wb") as f:
-    f.write(response.content)
-    print(response.content)
-f.close()
-
-# model_load_path = "model.pt"
-# model = BertForSTS()
-# model.load_state_dict(torch.load(model_load_path))
-# model.to(device)
-
-# def predict_similarity(sentence_pair):
-#   test_input = tokenizer(sentence_pair, padding='max_length', max_length = param_max_length, truncation=True, return_tensors="pt").to(device)
-#   test_input['input_ids'] = test_input['input_ids']
-#   print(test_input['input_ids'])
-#   test_input['attention_mask'] = test_input['attention_mask']
-#   del test_input['token_type_ids']
-#   output = model(test_input)
-#   sim = torch.nn.functional.cosine_similarity(output[0], output[1], dim=0).item()*2-1
-
-#   return sim
-
-# # Create a Gradio interface with a text input zone
-# iface = gr.Interface(
-#     fn=analyze_text,          # The function to be called with user input
-#     inputs=[gr.Textbox(), gr.Textbox()],
-#     outputs="text"            # Display the result as text
-# )
-
-# # # Launch the Gradio interface
-# iface.launch()
+# Define a function that takes a text input and returns the result
+def analyze_text(input):
+    # Your processing or model inference code here
+    result = predict_similarity(input)
+    return result
+
+param_model_name="CAMeL-Lab/bert-base-arabic-camelbert-msa-sixteenth"
+
+tokenizer = AutoTokenizer.from_pretrained(param_model_name)
+
+class BertForSTS(torch.nn.Module):
+
+    def __init__(self):
+        super(BertForSTS, self).__init__()
+        #self.bert = models.Transformer('bert-base-uncased', max_seq_length=128)
+        #self.bert = AutoModelForSequenceClassification.from_pretrained("CAMeL-Lab/bert-base-arabic-camelbert-msa-sixteenth")
+        self.bert = models.Transformer(param_model_name, max_seq_length=param_max_length)
+
+
+        dimension= self.bert.get_word_embedding_dimension()
+        #print(dimension)
+        self.pooling_layer = models.Pooling(dimension)
+        self.dropout = torch.nn.Dropout(0.1)
+
+        # relu activation function
+        self.relu =  torch.nn.ReLU()
+
+        # dense layer 1
+        self.fc1 = torch.nn.Linear(dimension,512)
+
+        # dense layer 2 (Output layer)
+        self.fc2 = torch.nn.Linear(512,512)
+        #self.pooling_layer = models.Pooling(self.bert.config.hidden_size)
+        self.sts_bert = SentenceTransformer(modules=[self.bert,self.pooling_layer, self.fc1])
+        #self.sts_bert = SentenceTransformer(modules=[self.bert,self.pooling_layer, self.fc1, self.relu, self.dropout,self.fc2])
+    def forward(self, input_data):
+        #print(input_data)
+        x=self.bert(input_data)
+        x=self.pooling_layer(x)
+        x=self.fc1(x['sentence_embedding'])
+        x = self.relu(x)
+        x = self.dropout(x)
+        #x = self.fc2(x)
+
+        return x
+
+# import requests
+
+# file_url = "https://drive.google.com/uc?export=download&id=1lDfR_B3fYM_rmC8H_HDtRdxJKveYWmOp"
+# response = requests.get(file_url)
+
+# with open("model.pt", "wb") as f:
+#     f.write(response.content)
+#     print(response.content)
+# f.close()
+
+model_load_path = "model.pt"
+model = BertForSTS()
+model.load_state_dict(torch.load(model_load_path))
+model.to(device)
+
+def predict_similarity(sentence_pair):
+  test_input = tokenizer(sentence_pair, padding='max_length', max_length = param_max_length, truncation=True, return_tensors="pt").to(device)
+  test_input['input_ids'] = test_input['input_ids']
+  print(test_input['input_ids'])
+  test_input['attention_mask'] = test_input['attention_mask']
+  del test_input['token_type_ids']
+  output = model(test_input)
+  sim = torch.nn.functional.cosine_similarity(output[0], output[1], dim=0).item()*2-1
+
+  return sim
+
+# Create a Gradio interface with a text input zone
+iface = gr.Interface(
+    fn=analyze_text,          # The function to be called with user input
+    inputs=[gr.Textbox(), gr.Textbox()],
+    outputs="text"            # Display the result as text
+)
+
+# # Launch the Gradio interface
+iface.launch()